Semantic Understanding of Scenes through ADE20K Dataset
Publication in refereed journal

香港中文大學研究人員
替代計量分析
.

其它資訊
摘要Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. Totally there are 25k images of the complex everyday scenes containing a variety of objects in their natural spatial context. On average there are 19.5 instances and 10.5 object classes per image. Based on ADE20K, we construct benchmarks for scene parsing and instance segmentation. We provide baseline performances on both of the benchmarks and re-implement state-of-the-art models for open source. We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance. We show that the networks trained on ADE20K are able to segment a wide variety of scenes and objects.
出版社接受日期28.11.2018
著者Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, Antonio Torralba
期刊名稱International Journal of Computer Vision
出版年份2019
月份3
卷號127
期次3
出版社Springer
頁次302 - 321
國際標準期刊號0920-5691
電子國際標準期刊號1573-1405
語言美式英語
關鍵詞Scene understanding, Semantic segmentation, Instance segmentation, Image dataset, Deep neural networks

上次更新時間 2021-18-09 於 23:54